A domain-specific perspective on adaptivity

Abstract

Adaptive support aims to tailor instruction to the needs of individual learners. Advances in technology have accelerated research on how such adaptivity can be achieved. While prior work demonstrates that adaptive support can enhance learning outcomes, most adaptive support relies on domain-general learner variables such as accuracy, response time, or behavioral engagement, treating learning processes largely as domain-general. But, in addition to the importance of domain-general principles of learning, decades of research in cognitive and educational psychology highlight that many learning processes are domain-specific, shaped by the conceptual, procedural, and epistemic structures of a particular domain. In this editorial, we argue that a domain-specific perspective on adaptivity is helpful for understanding when and how adaptive support is effective. Using Plass and Pawar’s (2020) framework, we discuss the role of domain specificity in three dimensions of adaptive learning environments: identifying learner variables to adapt for, measuring these variables, and adapting meaningfully in response. Domain-specific variables such as learners' misconceptions, their strategies, and their conceptual understanding require fine-grained assessment and theoretical models that link process data to learning mechanisms within a domain. Similarly, adapting instruction to these processes demands instructional models that account for domain-specific learning trajectories. The aim of this special issue was to collect empirical studies that use process data to study domain-specific adaptive learning. It consists of nine contributions from various subject domains and one discussion paper. Collectively, these contributions demonstrate both the promise and the complexity of grounding adaptivity in domain-specific learning processes and highlight important directions for future research.